Quantifying an implicit legal threshold and mental representations of ‘Innocent:’ a comparison of implicit and theoretical thresholds for a legal decision

ABSTRACT

This study aimed to clarify the standard for layperson’s legal decision and identify factors that contribute to establishing the criterion. Two samples were studied (undergraduate students and community members) to test the hypothesis that laypeople use a lower criterion for their legal decisions than the beyond a reasonable doubt (BRD) rule ordered by law. Relationships among individual’s implicit threshold (IT) for a legal decision and other psychological representations (e.g., not blameworthy, not guilty, desire to punish) were also analyzed. Participants read one of two or three vignettes describing a murder case and provided their verdicts, perceived likelihood of guilt ratings, and quantified mental representations of ‘innocent’ and BRD. In both studies, mean IT was lower than mean BRD. The concordance rate comparison in both studies showed that IT, rather than BRD, is a more accurate measure of laypeople’s legal decision thresholds. In Study 2, IT was positively correlated with representations of ‘innocent,’ suggesting that these representations of ‘innocent’ may be a significant factor that lowered the decision threshold. This study advances knowledge in the field by proposing a novel concept regarding layperson’s legal decision and exploring its relationships with other legal decision-making factors.

KEYWORDS:

• Implicit threshold
• beyond a reasonable doubt
• legal decision
• innocent
• guilty

Disclosure statement

No potential conflict of interest was reported by the author(s).

Data Availability Statement

The data that support the findings of this study are available from the corresponding author, upon reasonable request.

Notes

1. The confidence interval was calculated by the formula suggested in Snedecor and Cochran (1989), which is described as: ${\tau }_U,\hspace{0.17em}{\tau }_L=\hat{\tau }\pm \left[Z_{\alpha /2}\sqrt{\hat{\tau }\left(1-\hat{\tau }\right)/n}+1/\left(2n\right)\right]$, where $\hat{\tau }$ refers to the estimated concordance rate (classification rate in the original paper), and $n$ is the total number of cases.

Additional information

Funding

This work was supported by the Ministry of Education of the Republic of Korea and the National Research Foundation of Korea (NRF-2018S1A5B5A02030844).